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NanoNets
NanoNets

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Paperspace
Paperspace

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0
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NanoNets vs Paperspace: What are the differences?

NanoNets: Machine learning API with less data. Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application; Paperspace: The way to access and manage limitless computing power in the cloud. It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.

NanoNets and Paperspace belong to "Machine Learning as a Service" category of the tech stack.

Some of the features offered by NanoNets are:

  • Image categorization API with less than 30 images per category
  • Custom object localization API
  • Text deduplication API

On the other hand, Paperspace provides the following key features:

  • Intelligent alert
  • Two-factor authentication
  • Share drives
- No public GitHub repository available -
- No public GitHub repository available -

What is NanoNets?

Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

What is Paperspace?

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
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          What are some alternatives to NanoNets and Paperspace?
          Azure Machine Learning
          Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
          Amazon Machine Learning
          This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
          Amazon SageMaker
          A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
          Amazon Elastic Inference
          Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.
          FloydHub
          Platform-as-a-Service for training and deploying your DL models in the cloud. Start running your first project in < 30 sec! Floyd takes care of the grunt work so you can focus on the core of your problem.
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